{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e4G3AH9P4wYj4XlE5zMxsZkU+s/gK4CAASTsANwDnAG8GVkbEx4ratpmZNa9VXUOHAVdG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\nzKzi/j+JHU7ybsLJHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55b7a48950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X_train = np.array([\n",
    "    [158, 64],\n",
    "    [170, 86],\n",
    "    [183, 84],\n",
    "    [191, 80],\n",
    "    [155, 49],\n",
    "    [163, 59],\n",
    "    [180, 67],\n",
    "    [158, 54],\n",
    "    [170, 67]\n",
    "])\n",
    "y_train = ['male', 'male', 'male', 'male', 'female', 'female', 'female', 'female', 'female']\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Human Heights and Weights by Sex')\n",
    "plt.xlabel('Height in cm')\n",
    "plt.ylabel('Weight in kg')\n",
    "\n",
    "for i, x in enumerate(X_train):\n",
    "    plt.scatter(x[0], x[1], c='k', marker='x' if y_train[i] == 'male' else 'D')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
